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nduckmink

Arkon

by nduckmink
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Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between tools.

    Naming Consistency5/5

    The single tool uses a clear verb_noun pattern (extract_content), and with only one tool, there are no inconsistent naming conventions.

    Tool Count3/5

    A single tool is on the thin side, but for a focused content extraction purpose, it can be acceptable. However, it lacks the typical 3-15 tool scope.

    Completeness4/5

    The tool covers the core function of extracting content from both URLs and files. Some potential advanced features are missing, but for the stated purpose, it is reasonably complete.

  • Average 3.5/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 9 of 27 community issues answered or closed in the last 6 months
    • 38 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description bears full responsibility for disclosing behavior. However, it only mentions 'auto engine' without explaining output format, error handling, authentication requirements, or side effects. This leaves significant behavioral ambiguity.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, direct sentence that front-loads the core action and resource. It contains no fluff or redundant information, earning a perfect score for conciseness and structure.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool is relatively simple with two optional parameters and an output schema present, so the description covers the basic purpose. However, it does not clarify that at least one of url or file_path is expected, nor does it indicate what happens if both are omitted. This is a notable gap in completeness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema covers both parameters fully (100% coverage), so the baseline is 3. The description adds the relationship that the two parameters are alternatives ('a URL or file'), which is useful but not substantial. Most parameter meaning is already available in the schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses a specific verb 'extract' and clearly names the resource ('content from a URL or file'). It conveys the tool's primary function unambiguously. With no sibling tools present, there is no need for differentiation.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The phrase 'using Content Core's auto engine' implies the tool is for automated extraction, but there is no explicit guidance on when to use this tool versus alternatives. Since no sibling tools exist, the absence of explicit comparisons is acceptable, but context is still only implied.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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